In one conventional approach to industrial asset inspection, an unmanned aerial vehicles (UAV) is flown around industrial assets while obtaining pictures and/or collecting sensory data. The conventional approach implements UAV motion planning as tightly related to the targets-of-interest regions. Conventional motion planning methods implement a simple approach that consists of: defining the industrial asset's target(s)-of-interest; mapping a selected observation point to a corresponding waypoint on the path of a UAV; and connecting the waypoints in three dimensional space to generate the UAV's predetermined flight plan. This conventional UAV flight plan enables the UAV to fly a deliberate, preset path along the waypoints. At the waypoints, the UAV can acquire pictures of an industrial asset at viewing angles fixed in space by the flight plan.
Conventionally, a deliberative pre-flight planning process can be deployed to optimize the UAV's flight path to assure all waypoints can be achieved. This conventional, deliberative pre-flight planning results in a predetermined flight path that is not altered during the UAV's execution of the path. Such a locked flight plan cannot guarantee that all targets-of-interest can be captured during the actual inspection performed along the flight path.
This failure to capture all targets can be due to the imperfect knowledge of inspectors. For example, upgrades and/or reconfiguration can render knowledge of the industrial asset's real-world physical configuration inconsistent with the model used by conventional approaches to predetermine the UAV flight plan. Further, even if an accurate physical model is used to generate the model, an industrial asset can develop new corrosion areas, cracks, and other defects at locations unknown at the time the predetermined flight plan is rendered by the inspector setting the flight plan parameters. A UAV following a conventionally predetermined flight plan is incapable of autonomously altering its path based on real-time captured pictures and/or sensor data.